Pre-requisites for this course are basic probability and statistics, although two initial lectures of the master ML course (which is part of this PhD course) are devoted to a probability refresher.

Course Organization

1. The PhD students who wish to take the course will have to attend the first 12 lectures (first module, Prof. Bononi) of the Machine Learning For Pattern Recognition (MLPR) Course offered at the master course in Communication Engineering (Laurea Magistrale in CE, acronym: LMCE).
Please find here the Syllabus of Master ML course.
2. Then students will have a choice: A) either do an assignent project with Prof Cagnoni on topics related to the second part of the MLPR LMCE course,
   
   or
   
   B) attend the following 4 extra lectures (and pick one of these 4 topics for the oral exam):
   1. Density estimation and kNN
   2. Pricipal Component Analysis
   3. Exponential family and properties
   4. Conjugate priors and Estimation-Maximization (EM) algorithm (Bishop Ch 9)
   
   The extra lectures (and slides) are available in e-form in the Extra Video-lectures folder

This course (passed with the exam) is worth 4 credits (CFU). Audit is worth 1 CFU.

Exam is oral only, to be scheduled on an individual basis. When ready, please contact the instructor by email at alberto.bononi[AT]unipr. it and by specifying the requested date. The exam consists of solving some exercises and explaining theoretical details connected with them, for a total time of about 1 hour. You can bring your summary of important formulas in an A4 sheet to consult if you so wish.